Linear manifold modeling and graph estimation based on multivariate functional data with different coarseness scales

Eugen Pircalabelu and Gerda Claeskens
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Eugen Pircalabelu: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2022028, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: We develop a high-dimensional graphical modeling approach for functional data where the number of functions exceeds the available sample size. This is accomplished by proposing a sparse estimator for a concentration matrix when identifying linear manifolds. As such, the procedure extends the ideas of the manifold representation for functional data to high-dimensional settings where the number of functions is larger than the sample size. By working in a penalized setting it enriches the functional data framework by estimating sparse undirected graphs that show how functional nodes connect to other functional nodes. The procedure allows multiple coarseness scales to be present in the data and proposes a simultaneous estimation of several related graphs. Its performance is illustrated using a real-life fMRI dataset and with simulated data.

Keywords: Multiscale functional data; Graphical lasso; Joint estimation; Group penalty (search for similar items in EconPapers)
Pages: 22
Date: 2022-09-06
Note: In: Journal of Computational and Graphical Statistics, 2022
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2022028

DOI: 10.1080/10618600.2022.2108818

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